Multi-omics integration in biomedical research - A metabolomics-centric review. Review uri icon

Overview

abstract

  • Recent advances in high-throughput technologies have enabled the profiling of multiple layers of a biological system, including DNA sequence data (genomics), RNA expression levels (transcriptomics), and metabolite levels (metabolomics). This has led to the generation of vast amounts of biological data that can be integrated in so-called multi-omics studies to examine the complex molecular underpinnings of health and disease. Integrative analysis of such datasets is not straightforward and is particularly complicated by the high dimensionality and heterogeneity of the data and by the lack of universal analysis protocols. Previous reviews have discussed various strategies to address the challenges of data integration, elaborating on specific aspects, such as network inference or feature selection techniques. Thereby, the main focus has been on the integration of two omics layers in their relation to a phenotype of interest. In this review we provide an overview over a typical multi-omics workflow, focusing on integration methods that have the potential to combine metabolomics data with two or more omics. We discuss multiple integration concepts including data-driven, knowledge-based, simultaneous and step-wise approaches. We highlight the application of these methods in recent multi-omics studies, including large-scale integration efforts aiming at a global depiction of the complex relationships within and between different biological layers without focusing on a particular phenotype.

publication date

  • October 22, 2020

Research

keywords

  • Biomedical Research
  • Metabolomics

Identity

PubMed Central ID

  • PMC7701361

Scopus Document Identifier

  • 85094326952

Digital Object Identifier (DOI)

  • 10.1093/bioinformatics/btx656

PubMed ID

  • 33248648

Additional Document Info

volume

  • 1141